Ordinal Confidence Level Assignments for Regression Model Predictions.

IF 5.3 2区 化学 Q1 CHEMISTRY, MEDICINAL Journal of Chemical Information and Modeling Pub Date : 2024-12-23 Epub Date: 2024-12-09 DOI:10.1021/acs.jcim.4c01755
Steven Kearnes, Patrick Riley
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Abstract

We present a simple method for assigning accurate confidence levels to molecular property predictions from regression models. These confidence levels are easy to interpret and useful for making decisions in drug discovery programs. We demonstrate their performance using time-split validation with assay data from the Relay Therapeutics internal database.

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回归模型预测的有序置信水平分配。
我们提出了一种简单的方法,为回归模型的分子性质预测分配准确的置信水平。这些置信水平很容易解释,对药物发现项目的决策很有用。我们使用来自Relay Therapeutics内部数据库的分析数据的分时验证来证明它们的性能。
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来源期刊
CiteScore
9.80
自引率
10.70%
发文量
529
审稿时长
1.4 months
期刊介绍: The Journal of Chemical Information and Modeling publishes papers reporting new methodology and/or important applications in the fields of chemical informatics and molecular modeling. Specific topics include the representation and computer-based searching of chemical databases, molecular modeling, computer-aided molecular design of new materials, catalysts, or ligands, development of new computational methods or efficient algorithms for chemical software, and biopharmaceutical chemistry including analyses of biological activity and other issues related to drug discovery. Astute chemists, computer scientists, and information specialists look to this monthly’s insightful research studies, programming innovations, and software reviews to keep current with advances in this integral, multidisciplinary field. As a subscriber you’ll stay abreast of database search systems, use of graph theory in chemical problems, substructure search systems, pattern recognition and clustering, analysis of chemical and physical data, molecular modeling, graphics and natural language interfaces, bibliometric and citation analysis, and synthesis design and reactions databases.
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